The LLAB (l:c) colour model

Luo, M. Ronnier; Lo, Mei-Chun; Kuo, Wen-Guey

doi:10.1002/(sici)1520-6378(199612)21:6<412::aid-col4>3.0.co;2-z

Cited by 69 publications

(11 citation statements)

References 6 publications

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“…[2][3][4][5][6][7] This formula was developed by the Colour Measurement Committee (CMC), Society of Dyers and Colourists, Great Britain, and is referred to as the CMC (ᐉ:c) color difference formula. 8 ⌬E CMC(l:c) ϭ ͱͩ ⌬L*…”

Section: Color Difference (⌬E) Formulasmentioning

confidence: 99%

Color acceptance of direct dental restorative materials by human observers

Ragain¹,

2000

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The purposes of this study were to evaluate the CIELAB, CMC (2:1), and CMC (1:1) formulas to identify which provides the best indicator for acceptability of small color differences in the esthetic dental restorative materials, to determine if different groups of observers have different levels of acceptability, and to estimate the color difference that would indicate acceptability between a restoration and an adjacent tooth. The subject population of human observers was divided into four groups, each containing 12 subjects. The composition of the groups were: Group 1, dental auxiliaries and hygienists; Group 2, dentists; Group 3, dental materials scientists; and Group 4, patients. A color vision screening test was administered to each subject to ensure that only observers with normal color vision were evaluated. A composite resin color discrimination test was developed specifically for this study. This test was composed of six sets of discs fabricated from dental composite resin restorative materials. Each set consisted of one standard disc representing tooth color. In each set, six discs representing composite resin restorations were matched to the standard disc for a total of 36 pairings in the test. Color differences between the standard discs and the restoration discs were calculated in CIELAB, CMC (1:1), and CMC (2:1) color units. The subjects were asked to evaluate the composite resin materials as to acceptability of color differences between the disc pairs. The data were analyzed by logistic regression analysis for each observer and by each ⌬E formula to generate receiver operating characteristics (ROC) curves. The areas under these ROC curves were calculated and ranked. ANOVA and Tukey's Studentized Range (HSD) Test were applied to the ranks. In regard to the acceptance of dental restorations based solely on color difference, the CMC (1:1) color difference formula gave better correlation than the CIELAB formula for small color differences in the esthetic dental restorative materials. There were significant differences found between the experiment groups in regard to acceptability of color differences using the CMC (1:1) and CIELAB formulas. The dental hygienist/auxiliaries group proved to be more discriminating in accepting differences between tooth and composite resin restorative material color than patients. The mean 50:50 ⌬E replacement points for all subjects were 2.29 and 2.72 color units for the CMC (1:1) and CIELAB formulas, respectively.

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Section: Color Difference (⌬E) Formulasmentioning

confidence: 99%

Color acceptance of direct dental restorative materials by human observers

Ragain¹,

2000

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“…(3). So under the Bayesian framework, minimization of the regularized cost function F(w) ϭ ␣E w ϩ ␤E D is identical to finding the local most probable weight parameter w MP by means of maximizing the posterior probability P(w ͉ D, ␣, ␤, M i ).…”

Section: Bayesian Inference Of the Weight Parameters (Level 1)mentioning

confidence: 99%

“…The stateof-the-art colour-appearance model 1 CIECAM97s was adopted by CIE Technical Committee TC1-34 in May 1997. The CIECAM97s model incorporates features from various other models including Hunt, 2 LLAB (l :c), 3 and RLAB 4 models. All these models, in which chromatic transform, dynamic response function, and a colour space for representing the correlates of colour appearance dimensions were embedded, were developed with psychophysical data and physiological phenomena.…”

Section: Introductionmentioning

confidence: 99%

Colour-appearance modeling using feedforward networks with Bayesian regularization method? Part I: Forward model

Xin¹,

Shao²,

Chung³

2000

Color Res. Appl.

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In this article, a method of predicting colour appearance (from colorimetric attributes to colour-appearance attributes, i.e., forward model) using an artificial neural network is presented. The neural network model developed is a multilayer feedforward neural network model for predicting colour appearance (FNNCAM for short). The model was trained by LUTCHI colour-appearance datasets. The Levenberg-Marquardt algorithm is incorporated into the back-propagation procedure to accelerate the training of FNNCAM and the Bayesian regularization method is applied to the training of neural networks to improve generalization. The results of FNNCAM obtained are quite promising.

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“…For this example transformation, the Bradford chromatic adaptation transform 5 (BFD) will be used. The Bradford transform has been shown to produce accurate results 6,7 and has been adopted as part of the CIE recommended color appearance model 6 (CIECAM97s). The BFD transform typically includes a linear and a non-linear portion.…”

Section: Converting Images To Srgbmentioning

confidence: 99%

<title>JPEG 2000 file format provides flexible architecture for color encoding</title>

Houchin

Hauf

2000

SPIE Proceedings

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In the past, there have been a lot of different image file formats, providing a lot of different capabilities. However, the one thing they pretty much all shared was a very limited mechanism for encoding color. Storing a color image today forces developers to take either a "lowest-common-denominator" approach by using a single standard colorspace in all applications, or by using the capabilities of ICC color management at the loss of wide interoperability.The JPEG 2000 file format changes all this with a new architecture for encoding the colorspace of an image. While the solution is not perfect, it does greatly increase the number of colorspaces that can be encoded while maintaining a very high level of interoperability between applications. This paper describes the color encoding architecture in the JPEG 2000 file format and shows how this new architecture meets the needs of tomorrows imaging applications.

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The LLAB (l:c) colour model

Cited by 69 publications

References 6 publications

Color acceptance of direct dental restorative materials by human observers

Color acceptance of direct dental restorative materials by human observers

Colour-appearance modeling using feedforward networks with Bayesian regularization method? Part I: Forward model

<title>JPEG 2000 file format provides flexible architecture for color encoding</title>

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